class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _gen_b_hypos(
      self,
      b_hypos: List[Hypothesis],
      a_hypos: List[Hypothesis],
      next_token_probs: torch.Tensor,
      key_to_b_hypo: Dict[str, Hypothesis],
  ) -> List[Hypothesis]:
      for i in range(len(a_hypos)):
          h_a = a_hypos[i]
          append_blank_score = (
              _get_hypo_score(h_a) + next_token_probs[i, -1]
          )
          if _get_hypo_key(h_a) in key_to_b_hypo:
              h_b = key_to_b_hypo[_get_hypo_key(h_a)]
              _remove_hypo(h_b, b_hypos)
              score = float(
                  torch.tensor(_get_hypo_score(h_b)).logaddexp(
                      append_blank_score
                  )
              )
          else:
              score = float(append_blank_score)
          h_b = (
              _get_hypo_tokens(h_a),
              _get_hypo_predictor_out(h_a),
              _get_hypo_state(h_a),
              score,
              _get_hypo_trie(h_a),
          )
          b_hypos.append(h_b)
          key_to_b_hypo[_get_hypo_key(h_b)] = h_b
      _, sorted_idx = torch.tensor(
          [_get_hypo_score(hypo) for hypo in b_hypos]
      ).sort()
      return [b_hypos[idx] for idx in sorted_idx]
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 6 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._gen_b_hypos(b_hypos, a_hypos, next_token_probs, key_to_b_hypo) and returns: unknown